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2017SummerSeminar.htm
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<!DOCTYPE html>
<!-- saved from url=(0040)http://cs231n.stanford.edu/2016/syllabus -->
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<title>Stanford University CS231n: Convolutional Neural Networks for Visual Recognition</title>
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<a href="">
<h1>HCIL Winter Seminar 2017</h1>
</a>
<div style="clear:both;"></div>
</div>
<div class="sechighlight">
<div class="container sec">
<h2>Schedule and Syllabus</h2>
본 페이지는 2017년 여름 방학 동안 CNN 공부를 위한 HCIL 내부 세미나 일정입니다. <br/>
Stanford University의 <a href="http://cs231n.stanford.edu/2016/syllabus">CS231n: Convolutional Neural Networks for Visual Recognition (Winter 2016)</a>에 기반하였습니다. <br/>
<br/>
<a href="https://www.youtube.com/watch?v=yp9rwI_LZX8&list=PL16j5WbGpaM0_Tj8CRmurZ8Kk1gEBc7fg">[동영상 강의 URL]</a>
<br/>
<br/>
<b>세미나 기간:</b> 6월 19일 ~ 8월 31일 <br/>
<b>세미나 시간:</b> 매주 목요일 오후 1시 <br/>
<b>과제 제출 시간:</b> Due date의 오후 6시 (시간 엄수)<br/>
</div>
</div>
<div class="container sec">
<table class="table">
<tbody><tr class="active">
<th>Event Type</th><th>Due Date</th><th>Description</th><th>Course Materials</th>
</tr>
<tr>
<td>Lecture 1</td>
<td>June 28</td>
<td>Intro to Computer Vision, historical context.</td>
<td><a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture1.pdf">[slides]</a>
<!-- <a href="https://youtu.be/NfnWJUyUJYU"><b>[video]</b></a> -->
</td>
</tr>
<tr>
<td>Lecture 2</td>
<td>June 28</td>
<td>Image classification and the data-driven approach <br> k-nearest neighbor <br> Linear classification I</td>
<td><a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture2.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/8inugqHkfvE"><b>[video]</b></a> -->
<br>
<a href="http://cs231n.github.io/python-numpy-tutorial">[python/numpy tutorial]</a><br>
<a href="http://cs231n.github.io/classification">[image classification notes]</a><br>
<a href="http://cs231n.github.io/linear-classify">[linear classification notes]</a><br>
</td>
</tr>
<tr class="warning">
<td>A1-part1 Due</td>
<td>June 28</td>
<td>Assignment #1 (Q1, Q2) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment1/">[Assignment #1]</a></td>
</tr>
<tr>
<td>Lecture 3</td>
<td>July 5</td>
<td>
Linear classification II<br>
Higher-level representations, image features<br>
Optimization, stochastic gradient descent</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture3.pdf">[slides]</a> [video]
<!-- <a href="https://www.youtube.com/watch?v=qlLChbHhbg4"><b>[video]</b></a> -->
<br>
<a href="http://cs231n.github.io/linear-classify">[linear classification notes]</a><br>
<a href="http://cs231n.github.io/optimization-1">[optimization notes]</a>
</td>
</tr>
<tr>
<td>Lecture 4</td>
<td>July 5</td>
<td>Backpropagation<br>
Introduction to neural networks</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture4.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/i94OvYb6noo"><b>[video]</b></a> -->
<br>
<a href="http://cs231n.github.io/optimization-2">[backprop notes]</a><br>
<a href="http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf">[Efficient BackProp]</a> (optional)<br>
related: <a href="http://colah.github.io/posts/2015-08-Backprop/">[1]</a>, <a href="http://neuralnetworksanddeeplearning.com/chap2.html">[2]</a>, <a href="https://www.youtube.com/watch?v=q0pm3BrIUFo">[3]</a> (optional)
</td>
</tr>
<tr class="warning">
<td>A1-part2 Due</td>
<td>July 5</td>
<td>Assignment #1 (Q3, Q4, Q5) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment1/">[Assignment #1]</a></td>
</tr>
<tr>
<td>Lecture 5</td>
<td>July 12</td>
<td>Training Neural Networks Part 1<br>
activation functions, weight initialization, gradient flow, batch normalization<br>
babysitting the learning process, hyperparameter optimization
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture5.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/gYpoJMlgyXA"><b>[video]</b></a> -->
<br>
<a href="http://cs231n.github.io/neural-networks-1/">Neural Nets notes 1</a><br>
<a href="http://cs231n.github.io/neural-networks-2/">Neural Nets notes 2</a><br>
<a href="http://cs231n.github.io/neural-networks-3/">Neural Nets notes 3</a><br>
tips/tricks:
<a href="http://research.microsoft.com/pubs/192769/tricks-2012.pdf">[1]</a>,
<a href="http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf">[2]</a>,
<a href="http://arxiv.org/pdf/1206.5533v2.pdf">[3]</a> (optional)
<br>
<a href="http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html">Deep Learning [Nature]</a> (optional)
</td>
</tr>
<tr class="warning">
<td>A2-part1 Due</td>
<td>July 12</td>
<td>Assignment #2 (Q1) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment2/">[Assignment #2]</a></td>
</tr>
<tr>
<td>Lecture 6</td>
<td>July 19</td>
<td>
Training Neural Networks Part 2: parameter updates, ensembles, dropout<br>
Convolutional Neural Networks: intro
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture6.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/hd_KFJ5ktUc"><b>[video]</b></a> -->
<br>
<a href="http://cs231n.github.io/neural-networks-3/">Neural Nets notes 3</a><br>
</td>
</tr>
<tr>
<td>Lecture 7</td>
<td>July 19</td>
<td>
Convolutional Neural Networks: architectures, convolution / pooling layers<br>
Case study of ImageNet challenge winning ConvNets
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture7.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/LxfUGhug-iQ"><b>[video]</b></a> -->
<br>
<a href="http://cs231n.github.io/convolutional-networks/">ConvNet notes</a><br>
</td>
</tr>
<tr class="warning">
<td>A2-part2 Due</td>
<td>July 19</td>
<td>Assignment #2 (Q2, Q3) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment2/">[Assignment #2]</a></td>
</tr>
<tr>
<td>Lecture 8</td>
<td>July 26</td>
<td>
ConvNets for spatial localization<br>
Object detection</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/GxZrEKZfW2o"><b>[video]</b></a> -->
</td>
</tr>
<tr>
<td>Lecture 9</td>
<td>July 26</td>
<td>
Understanding and visualizing Convolutional Neural Networks<br>
Backprop into image: Visualizations, deep dream, artistic style transfer<br>
Adversarial fooling examples<br>
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture9.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/ta5fdaqDT3M"><b>[video]</b></a> -->
</td>
</tr>
<tr class="warning">
<td>A2-part3 Due</td>
<td>July 26</td>
<td>Assignment #2 (Q4) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment2/">[Assignment #2]</a></td>
</tr>
<tr>
<td>Lecture 10</td>
<td>Aug 2</td>
<td>
Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM)<br>
RNN language models<br>
Image captioning
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture10.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/yCC09vCHzF8"><b>[video]</b></a> -->
<br>
<a href="http://www.deeplearningbook.org/contents/rnn.html">DL book RNN chapter</a> (optional)<br>
<a href="https://gist.github.com/karpathy/d4dee566867f8291f086">min-char-rnn</a>, <a href="https://github.com/karpathy/char-rnn">char-rnn</a>, <a href="https://github.com/karpathy/neuraltalk2">neuraltalk2</a>
</td>
</tr>
<tr class="warning">
<td>A3-part1 Due</td>
<td>Aug 2</td>
<td>Assignment #3 (Q1) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment3/">[Assignment #3]</a></td>
</tr>
<tr>
<td>Lecture 11</td>
<td>Aug 9</td>
<td>
Training ConvNets in practice<br>
Data augmentation, transfer learning<br>
Distributed training, CPU/GPU bottlenecks<br>
Efficient convolutions<br>
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture11.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/pA4BsUK3oP4"><b>[video]</b></a> -->
</td>
</tr>
<tr class="warning">
<td>A3-part2 Due</td>
<td>Aug 9</td>
<td>Assignment #3 (Q2) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment3/">[Assignment #3]</a></td>
</tr>
<tr>
<td>Lecture 12</td>
<td>Aug 16</td>
<td>
Overview of Caffe/Torch/Theano/TensorFlow
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture12.pdf">[slides]</a> [video]
<!-- <a href="https://youtu.be/Vf_-OkqbwPo"><b>[video]</b></a> -->
</td>
</tr>
<tr class="warning">
<td>A3-part3 Due</td>
<td>Aug 16</td>
<td>Assignment #3 (Q3) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment3/">[Assignment #3]</a></td>
</tr>
<tr>
<td>Lecture 13</td>
<td>Aug 23</td>
<td>
Segmentation<br>
Soft attention models<br>
Spatial transformer networks<br>
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture13.pdf">[slides]</a> [video]
<!-- <a href="https://www.youtube.com/watch?v=ByjaPdWXKJ4"><b>[video]</b></a> -->
</td>
</tr>
<tr>
<td>Lecture 14</td>
<td>Aug 23</td>
<td>
ConvNets for videos<br>
Unsupervised learning<br>
</td>
<td>
<a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture14.pdf">[slides]</a> [video]
<!-- <a href="https://www.youtube.com/watch?v=ekyBklxwQMU"><b>[video]</b></a> -->
</td>
</tr>
<tr class="warning">
<td>A3-part4 Due</td>
<td>Aug 23</td>
<td>Assignment #3 (Q4) Due date</td>
<td><a href="http://cs231n.github.io/assignments2016/assignment3/">[Assignment #3]</a></td>
</tr>
<tr>
<td>Lecture 15</td>
<td>Aug 30</td>
<td>
Invited Talk: <a href="http://en.wikipedia.org/wiki/Jeff_Dean_(computer_scientist)">Jeff Dean</a> [video]
</td><td>
<!-- <a href="https://www.youtube.com/watch?v=T7YkPWpwFD4"><b>[video]</b></a> -->
Optional
</td>
</tr>
<tr class="danger">
<td>Proposal</td>
<td>Aug 30</td>
<td>이후 본인 과제에 어떻게 적용할지를 1 paragraph로 정리해서 PDF로 제출</td>
<td><a href="">[제출]</a></td>
</tr>
</tbody></table>
</div>
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